Linear Convergence of First- and Zeroth-Order Primal-Dual Algorithms for Distributed Nonconvex Optimization

This article considers the distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of local cost functions by using local information exchange. We first consider a distributed first-order primal-dual algorithm. We show that it converges sublinearly to a statio...

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Veröffentlicht in:	IEEE transactions on automatic control Jg. 67; H. 8; S. 4194 - 4201
Hauptverfasser:	Yi, Xinlei, Zhang, Shengjun, Yang, Tao, Chai, Tianyou, Johansson, Karl H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Convergence Convex functions Convexity Cost function Costs Distributed nonconvex optimization First order algorithms first-order algorithm Laplace equations linear convergence Lyapunov methods Optimization primal-dual algorithm Technological innovation zeroth-order algorithm zerothorder algorithm
ISSN:	0018-9286, 1558-2523, 1558-2523
Online-Zugang:	Volltext
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